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| -rw-r--r-- | aweight.py | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/aweight.py b/aweight.py new file mode 100644 index 0000000..3c06bcd --- /dev/null +++ b/aweight.py @@ -0,0 +1,48 @@ +from scipy import signal +from waveform_analysis import A_weighting +from math import pi +import numpy +import matplotlib.pyplot as plt + + +# Create our X axis values, the frequencies to test at: +gs = 2 * pi * numpy.geomspace(10, 24000, 1000) + + +# This is the ideal A-weighting filter: +z, p, k = A_weighting(48000, output='zpk') +_, hA = signal.freqz_zpk(z, p, k, gs, fs=48000) + + +# And here is our three-stage implementation: +z, p, k = signal.iirfilter(1, [640, 10000], btype='bandpass', analog=False, fs=48000, output='zpk') +_, hB = signal.freqz_zpk(z, p, k * 1.17, gs, fs=48000) + +z, p, k = signal.iirfilter(1, 12000, btype='lowpass', analog=False, fs=48000, output='zpk') +_, hL = signal.freqz_zpk(z, p, k, gs, fs=48000) + +z, p, k = signal.iirfilter(1, 120, btype='highpass', analog=False, fs=48000, output='zpk') +_, hH = signal.freqz_zpk(z, p, k, gs, fs=48000) + + +# Lastly, our SOS approximation: +sos = [[ 0.5, 0, -0.5, 1, -1.062, 0.14], + [ 0.5, 0.5, 0, 1, 0, 0 ], + [ 1.0, -1.0, 0, 1, -0.985, 0 ]] +wS, hS = signal.sosfreqz(sos, fs=48000) + + +# Plot everything: +plt.semilogx(gs, 20 * numpy.log10(hA), label='A') +plt.semilogx(gs, 20 * numpy.log10(hB * hL * hH), label='bp+lp+hp') +plt.semilogx(wS, 20 * numpy.log10(hS), label='sos') + +plt.title('Frequency response') +plt.xlabel('Frequency [Hz]') +plt.ylabel('Amplitude [dB]') +plt.ylim(-30, 10) +plt.xlim(20, 24000) +plt.grid(True, color='0.7', linestyle='-', which='major', axis='both') +plt.grid(True, color='0.9', linestyle='-', which='minor', axis='both') +plt.legend() +plt.show() |